TY - GEN
T1 - PPP
T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
AU - Wang, Yilin
AU - Wang, Suhang
AU - Tang, Jiliang
AU - Liu, Huan
AU - Li, Baoxin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/9
Y1 - 2016/12/9
N2 - Pointwise label and pairwise label are both widely used in computer vision tasks. For example, supervised image classification and annotation approaches use pointwise label, while attribute-based image relative learning often adopts pairwise labels. These two types of labels are often considered independently and most existing efforts utilize them separately. However, pointwise labels in image classification and tag annotation are inherently related to the pairwise labels. For example, an image labeled with 'coast' and annotated with 'beach, sea, sand, sky' is more likely to have a higher ranking score in terms of the attribute 'open', while 'men shoes' ranked highly on the attribute 'formal' are likely to be annotated with 'leather, lace up' than 'buckle, fabric'. The existence of potential relations between pointwise labels and pairwise labels motivates us to fuse them together for jointly addressing related vision tasks. In particular, we provide a principled way to capture the relations between class labels, tags and attributes, and propose a novel framework PPP(Pointwise and Pairwise image label Prediction), which is based on overlapped group structure extracted from the pointwise-pairwise-label bipartite graph. With experiments on benchmark datasets, we demonstrate that the proposed framework achieves superior performance on three vision tasks compared to the state-of-the-art methods.
AB - Pointwise label and pairwise label are both widely used in computer vision tasks. For example, supervised image classification and annotation approaches use pointwise label, while attribute-based image relative learning often adopts pairwise labels. These two types of labels are often considered independently and most existing efforts utilize them separately. However, pointwise labels in image classification and tag annotation are inherently related to the pairwise labels. For example, an image labeled with 'coast' and annotated with 'beach, sea, sand, sky' is more likely to have a higher ranking score in terms of the attribute 'open', while 'men shoes' ranked highly on the attribute 'formal' are likely to be annotated with 'leather, lace up' than 'buckle, fabric'. The existence of potential relations between pointwise labels and pairwise labels motivates us to fuse them together for jointly addressing related vision tasks. In particular, we provide a principled way to capture the relations between class labels, tags and attributes, and propose a novel framework PPP(Pointwise and Pairwise image label Prediction), which is based on overlapped group structure extracted from the pointwise-pairwise-label bipartite graph. With experiments on benchmark datasets, we demonstrate that the proposed framework achieves superior performance on three vision tasks compared to the state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=84986321986&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986321986&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2016.646
DO - 10.1109/CVPR.2016.646
M3 - Conference contribution
AN - SCOPUS:84986321986
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 6005
EP - 6013
BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PB - IEEE Computer Society
Y2 - 26 June 2016 through 1 July 2016
ER -